IBM Big Data Hub

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

Smarten

JANUARY 31, 2019

Today’s Advanced Analytics Tools allow business users to leverage features like self-serve data preparation, smart data visualization and assisted predictive modeling. Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever.

Business Intelligence 40

Business Intelligence Analytics Predictive Modeling Forecasting 40

Sisense

AUGUST 11, 2019

BA is a catch-all expression for approaches and technologies you can use to access and explore your company’s data, with a view to drawing out new, useful insights to improve business planning and boost future performance. BA primarily predicts what will happen in the future. What About “Business Intelligence”? Choosing the Right Tech.

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

FineReport

MAY 13, 2020

KNIME is an open-source BI tool specialized for data linkage, integration, and analysis. It provides data scientists and BI executives with data mining, machine learning, and data visualization capabilities to build effective data pipelines. . 1 in China’s BI market share in H1 2019. From Google.

OLAP 105

OLAP KPI Data mining Business Intelligence 105

datapine

MARCH 25, 2022

There is not a clear line between business intelligence and analytics, but they are extremely connected and interlaced in their approach towards resolving business issues, providing insights on past and present data, and defining future decisions. Most BI software in the market are self-service. BI and BA Use-Case Scenarios?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Rocket-Powered Data Science

JULY 6, 2021

Customer purchase patterns, supply chain, inventory, and logistics represent just a few domains where we see new and emergent behaviors, responses, and outcomes represented in our data and in our predictive models.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Smarten

DECEMBER 12, 2018

Many businesses are just discovering the benefits of self-serve business intelligence and establishing data democratization initiatives but, as every business manager and team member knows, business markets and competition move rapidly and yesterday’s business intelligence initiatives are morphing into advanced analytics efforts.

Analytics 40

Analytics Business Intelligence Data mining Predictive Modeling 40

datapine

NOVEMBER 27, 2019

Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among the BI professionals, especially since big data is becoming the main focus of analytics processes that are being leveraged not just by big enterprises, but small and medium-sized businesses alike.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

FineReport

MARCH 31, 2024

These tools empower organizations to glean valuable insights from their data, enhancing decision-making processes and bolstering competitiveness in data-driven markets. Best BI Tools for Data Analysts 3.1 Why is Choosing the Best BI Tools Important?

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

Jet Global

MAY 1, 2023

Section 2: Embedded Analytics: No Longer a Want but a Need Section 3: How to be Successful with Embedded Analytics Section 4: Embedded Analytics: Build versus Buy Section 5: Evaluating an Embedded Analytics Solution Section 6: Go-to-Market Best Practices Section 7: The Future of Embedded Analytics Section 1: What are Embedded Analytics?

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52